612 research outputs found
ラバーウッド : その供給特性と利用の発展
While the sustainability of timber production in natural forests in Southeast Asia has seemed to be in peril, rubberwood, a by-product of natural rubber production, has attracted more interest and its utilization steadily developed through the 1980's. In the 1990's, however, especially in countries like Malaysia where utilization expanded intensively, a shortage problem has emerged and prices have increased. Since rubberwood production is a secondary concern for farmers, even when there is increased demand the supply doesn't respond to it. This is a problem for rubberwood supply as an industrial resource, differing from other timbers.天然林からの持続的な木材生産が危ぶまれている東南アジアにおいて、天然ゴム(ラテックス)を産出するパラゴムノキの木部ラバーウッドが、1980年代から着実に需要を伸ばしている。資源量が豊富で価格が安いことに加え、あくまでもラテックスを採取した後の廃材を有効に利用しているがゆえに、ラバーウッドは理想的な木材として期待を集め、特に木製家具産業において大量に利用されている。ところが、ラバーウッドは、現時点では価格が低すぎ、生産者は積極的な生産意欲を持たないため、廃材として供給される以上の量が市場に出てこない。つまり、ラバーウッドの供給は、需要の増減に弾力的に反応しないという特性を持つために、需要が拡大したマレーシアでは不足状況が生じ、利用者にとって価格上昇が深刻な問題となりつつある
One consideration on the wind pressure load acting on the surface of mesh membrane structure. Comparison of experiment and analysis on a simple shape
p. 2666-2678In this paper, we have obtained wind force coefficients acting on a membrane roof by the
wind tunnel experiment technique and a numerical analysis technique for free-standing
canopy with mesh membrane, and will show one consideration on an approach of the
numerical analysis to get wind pressure distributions by comparing values of the wind force coefficients that obtained with these techniques. The roof type treated in the paper is a mono-sloped roof. The mesh membrane is porous on the surface, so air of the wind can go through it. This phenomenon affects the wind force coefficients, and this point is a characteristic of mesh membrane. Therefore, it is necessary to reflect this phenomenon on the surface of a model using in the wind tunnel experiment. For the reason, a coefficient on wind pressure loss of the mesh membrane to be called the loss coefficient was actually measured by an experiment, and was reflected the roof model. Furthermore, this loss coefficient was used at the analysis. The measurement experiment to obtain this pressure loss coefficient will be introduced also.Takeda, F.; Yoshino, T.; Uematsu, Y. (2009). One consideration on the wind pressure load acting on the surface of mesh membrane structure. Comparison of experiment and analysis on a simple shape. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/660
白血病細胞の増殖動態に関する研究: 生体内における細胞回転の解析
取得学位 : 博士(医学), 学位授与番号 : 医博甲第965号, 学位授与年月日:平成2年9月30日,学位授与年:199
Design wind force coefficients for freestanding canopy roofs of membrane structures
The present study examines the wind-induced behaviour of free-standing membrane canopy roofs with various types of roof-supporting systems and presents wind force coefficients for designing such roofs. The effects of roof deformation and choice of roof-supporting system on wind force coefficients are investigated. The characteristics of wind-induced responses of the roofs are taken into account for improving the wind force coefficients that we previously proposed based on the results of wind tunnel experiment with rigid roof models
Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT
Wada S., Takeda T., Okada K., et al. Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT. Artificial Intelligence in Medicine 153, 102889 (2024); https://doi.org/10.1016/j.artmed.2024.102889.Background: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. Objective: We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. Methods: Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. Results: Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. Conclusions: Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model
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